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All Journal JUITA : Jurnal Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal ICT : Information Communication & Technology
Nita Helmawati
Universitas Amikom Yogyakarta
Computer Science & IT Engineering

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Analisis Breadth-First Search dan Algoritma Certainty Factor untuk Diagnosa Penyakit Pada Mahasiswa Norhikmah; Nita Helmawati; Wiji Nurastuti
Jurnal ICT: Information Communication & Technology Vol. 23 No. 1 (2023): JICT-IKMI, Juli 2023
Publisher : LPPM STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The problem that often occurs, especially among students, is a lack of knowledge about disease symptoms, which can lead to difficulties in making an initial diagnosis and require assistance from medical experts. Students often have busy schedules and do not have enough time to undergo regular health check-ups, resulting in symptoms of diseases being overlooked and not detected quickly. Some students may not have access to adequate healthcare services, especially those living in remote areas or outside the city. Students often do not realize the importance of maintaining their health and undergoing regular health check-ups, which can worsen their health conditions. Therefore, a system is needed to assist students in quickly and accurately diagnosing diseases. This research aims to develop a disease diagnosis system for students using the breadth-first search method and certainty factor algorithm. This method utilizes calculations based on similarity divided by predetermined weights. Certainty factor (CF) is a clinical parameter value provided by experts to indicate the degree of confidence in a fact or rule. In this study, disease symptoms are inputted into an expert system and calculated using the certainty factor method to diagnose the type of disease suffered by students. The research results show that the developed expert system successfully diagnoses the type of disease with an accuracy of 97.5%.
Analysis for Detecting Banana Leaf Disease Using the CNN Method Nita Helmawati; Ema Utami
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 1, March 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i1.24514

Abstract

Banana farmers face major challenges due to banana leaf diseases such as Cordana, Pestalotiopsis and Sigatoka, which severely affect the quality and quantity of the crop. Early detection of these diseases is particularly challenging as the initial symptoms are often similar to other disorders. To solve this problem, fast and accurate automated detection is needed to help farmers effectively identify diseases on banana leaves. This research focuses on developing a banana leaf disease detection model using Convolutional Neural Network (CNN) method with MobileNetV2 architecture. The dataset used consists of 937 images of both infected and healthy banana leaves. These images were collected under various lighting conditions and viewing angles to simulate real field situations. The dataset was divided into 70% for training, 20% for validation, and 10% for testing, to ensure robust model evaluation. The CNN model was trained to recognize important visual features on banana leaves that indicate disease infection. The results showed that the model was able to detect banana leaf diseases with an accuracy of 90.62%, indicating high effectiveness. This accuracy confirms the potential of CNN in significantly improving the disease detection process on banana plants. This research is expected to help farmers identify diseases more quickly and accurately, thereby minimizing yield losses and increasing productivity. In addition, this research provides valuable insights into the application of technology in agriculture, particularly in plant disease detection which opens up opportunities for further advancements in this sector.
Utilization of the Convolutional Neural Network Method for Detecting Banana Leaf Disease Nita Helmawati; Ema Utami
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.6140

Abstract

Banana leaf diseases such as Sigatoka, Cordana, and Pestalotiopsis pose a significant threat to banana productivity, with implications for food security and the global economy. Early detection of this disease is an important step to reduce its spread and maintain crop yield stability. This research utilizes the Convolutional Neural Network (CNN) method to detect banana leaf diseases based on image analysis of infected and healthy leaves. The dataset used includes 937 images consisting of four main categories, namely healthy leaves, Sigatoka, Cordana, and Pestalotiopsis. The dataset is processed through augmentation to increase data diversity and quality. The CNN model was applied for classification, with evaluation results reaching 92.85% accuracy, 95.73% recall, 93.52% precision, and 94.60% F1-score. This research contributes to the development of Artificial Intelligence-based technology for applications in the agricultural sector, especially in supporting farmers to detect banana leaf diseases quickly, accurately and efficiently. The research results also provide recommendations for exploring additional data augmentation and increasing dataset variety to improve model detection performance in the future. This shows CNN's potential in supporting sustainable agriculture in the modern era.
Deep Learning-Based Soybean Leaf Disease Classification Using DenseNet121, Xception, and MobileNetV2 Nita Helmawati; Yopy Tri Buana; Eko Rahmad Darmawan; Kusrini Kusrini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6271

Abstract

This study is driven by the challenge of soybean leaf diseases, which significantly reduce agricultural productivity and pose a threat to food security. To address this issue, we developed a deep learning–based classification model for soybean leaf disease detection, employing three prominent architectures: DenseNet121, Xception, and MobileNetV2. The dataset comprised 770 images representing six disease categories and one healthy category, which was expanded to 5,880 images using data augmentation techniques. The dataset was evaluated under three experimental scenarios with splits of 70% training, 10% validation, and 20% testing. Experimental results demonstrated that the DenseNet121 model, optimized with AdamW, achieved the highest accuracy at 90.14%, outperforming MobileNetV2 (85.48%) and Xception (65.37%). Moreover, DenseNet121 exhibited the most consistent performance in classifying the diverse categories of soybean leaf diseases.
Co-Authors Eko Rahmad Darmawan Ema Utami Kusrini Norhikmah Norhikmah Wiji Nurastuti Yopy Tri Buana